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Accurate rendering of diagnosis and prognosis for a disease with respect to a patient requires analysis of complicated, diverse, yet correlated risk factors (RFs). Most of the existing methods for this purpose are based on handcraft RFs by calculating their statistical significance to the disease. However, such methods not only incur intensive labor but also lack capability to discover or infer previously unknown complex relationships and combined effects among correlated RFs.
Suo et al. (Sun,) studied this question.